Abstract 1211: Allele-specific copy-number based deconvolution of bulk tumour RNA sequencing data from the TRACERx study
Authors: Carla Castignani, Jonas Demeulemeester, Elizabeth Larose Cadieux, Robert E. Hynds, David R. Pearce, Stefan C. Dentro, Peter Van Loo, Charles Swanton, TRACERx Consortium
Journal: Cancer Research (2022)

Abstract Introduction: Analyses of bulk RNA sequencing (RNA-seq) data are central to most large-scale tumour sequencing studies. Solid tumors often present a dynamic, heterogeneous environment consisting of both cancer (sub)clones and normal cells. Bulk expression data represent population averages and its interpretation is confounded by both normal cell contamination and somatic copy number alterations. Several computational methods to deconvolve tumour and normal expression profiles from bulk RNA-seq have been developed recently. However, these methods often rely on a set of cell-type-specific reference signatures and ignore the effect of copy number changes. Methods: To address these issues, we have developed a method that formalizes the relationship between allele-specific copy number, expression and sample purity to deconvolve the expression profiles of tumor and normal cells from bulk RNA-seq data in an unbiased manner. Our method was applied to sequencing data produced by the TRACERx consortium, a longitudinal study with multi-region whole-exome and RNA-seq of non-small-cell lung cancers. A total of 414 primary tumor regions and 140 adjacent normal tissue samples from 140 TRACERx patients with matched DNA and RNA sequencing data were processed. Results: Here, we were able to directly deconvolve a median of ~2,000 genes per sample and indirectly infer tumor and normal expression profiles of ~10,000 genes. The accuracy of the deconvolution was validated using in-silico mixtures of patient-derived tumour and normal cells and in regions with loss of heterogeneity (LOH) directly on the bulk sequencing data, where the total fraction of expression attributed to tumor cells can be computed directly using somatic mutations. Our method revealed a strong and constitutive genome-wide overexpression in cancer cells compared to admixed normal cells, this overexpression was more pronounced in lung squamous cell carcinoma than lung adenocarcinoma (p&amp;lt0.001). Multidimensional projection of the purified tumor and normal expression profiles together with the adjacent normal tissue showed clear separation between the purified expression profiles and notably, the deconvolved normal expression was more similar to the normal adjacent profiles than the purified tumor profiles. Conclusion: Overall, these results suggest that our method is able to accurately disentangle the expression of tumour and normal cells from bulk RNA-seq without any previous knowledge. It has potential applications in many studies that include matched RNA-seq and copy number data and can provide new insights functional characterization, the taxonomy of cancer, and tumor evolution. Citation Format: Carla Castignani, Jonas Demeulemeester, Elizabeth Larose Cadieux, Robert E. Hynds, David R. Pearce, Stefan C. Dentro, Peter Van Loo, Charles Swanton, TRACERx Consortium. Allele-specific copy-number based deconvolution of bulk tumour RNA sequencing data from the TRACERx study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1211.